Your Analytics are Double-Counting Your Users—and It’s Costing You Money

The word ‘analytics’ often covers a lot of sins. That is to say, many executives will follow their analytics blindly, mainly because algorithmically-attained numbers create the impression of objectivity and certainty. 

But just because something is analytical doesn’t always mean it’s factual. Indeed, if you’re using analytics to track your cross-platform customer journey – perhaps one of the most important aspects of modern digital marketing – it’s very likely that your analytics are lying to you. Not maliciously; structurally. And all because they count devices instead of people. 

The Cost Of Your Data

So what does that mean? Well, imagine a customer sees one of your ads on social media one morning, and imagine it sticks with them. Later that evening, they browse your website using their laptop, and the next day, they download your app and make a purchase from their smartphone.

To a human being, that’s obviously one customer journey. But to many analytics systems, it looks like three separate users interacting independently across different environments. 

The result is a fragmented view of reality. You have 1,000 ‘users’ in your analytics, but you actually only have 300 real customers. In other words, you’re overcounting users and undercounting the value of your ads.

This is a problem, because when you treat each device as a separate customer, your acquisition numbers become inflated, your attribution becomes distorted, and your understanding of customer behavior starts drifting further away from how people actually make decisions. 

In some cases, businesses can end up dramatically underestimating the value of entire marketing channels, or even worse, end up allocating their budget inefficiently – pouring money into channels that look like they convert well in isolation while underfunding the ones that actually initiate or influence the journey.

Over time, that means higher customer acquisition costs and wasted ad spend, simply because the underlying measurement is misattributing value.

The Issue With Device-Centric Analytics

This is the real issue with device-centric analytics: they measure touchpoints accurately, sure, but they often fail to measure people accurately.

Because people don’t live on one device. They watch CTV ads, they browse on laptops, they buy on apps – or perhaps they browse on their smartphone, get reintroduced to your brand through CTV, and buy on their laptop.

The point is, your current tech sees three different people, and as a result, your business decisions are being made as though those fragmented interactions belong to three separate customers with three separate journeys.

The important thing to understand is this: you aren’t running a ‘mobile’ or a ‘web’ business; you’re running a human business. What does this mean? It means recognizing that customers don’t think in channels, platforms, or devices in the same way that you do.

To the customer, the journey feels continuous. They aren’t consciously separating their experience into ‘mobile engagement ‘ or ‘desktop conversion’ – they’re simply interacting with the brand whenever and wherever it’s convenient for them. 

Right now, you’re structuring your analytics around the opposite assumption, so in order to grow, you need to start measuring the user journey, not just the device.

By connecting mobile, web, CTV, and PC touchpoints back to their original source, you stop chasing phantom users and start seeing clear LTV insights, and this can be done by implementing the right platform and building your measurement systems appropriately. 

A Leader In The Field

One of the leaders in this field is Appsflyer, who approach cross platform measurement by combining attribution infrastructure, identity matching, and event tracking into a unified framework.

In practical terms, this means collecting engagement data from multiple sources and working to connect those interactions into something far closer to a continuous customer journey. 

Attribution, for instance, is achieved by tracking clicks and customer conversion rates, and linking them back to marketing campaigns using defined attribution rules and time windows, while identity matching uses a mix of device signals and login data to identify when different devices or sessions belong to the same user.

How This Helps

The result is a clear understanding of how your marketing actually performs in today’s digital environment. When monitoring your website and noticing a spike in traffic or conversions from a particular source, instead of guessing which campaigns are working – or assuming based on incomplete data – you can start to see how different touchpoints contribute across the full journey, and this is so important when it comes to your overall decision-making.

Every budget shift, campaign adjustment, or channel optimization is now based on how the customers actually behave rather than how your systems happen to record their behavior. Over time, then, this can lead to far more efficient acquisition spending and a higher lifetime value of customers. 

Conclusion

In the digital ecosystem, attention is scattered and customer journeys can take all sorts of unexpected turns. The number one thing you need is clarity, and by turning your data into something more coherent and connected, that’s exactly what you’ll get.